Pairing a lexical retriever with a neural re-ranking model has set
state-of-the-art performance on large-scale information retrieval datasets.
This pipeline covers scenarios like question answering or navigational queries,
however, for information-seeking scenarios, users often provide information on
whether a document is relevant to their query in form of clicks or explicit
feedback. Therefore, in this work, we explore how relevance feedback can be
directly integrated into neural re-ranking models by adopting few-shot and
parameter-efficient learning techniques. Specifically, we introduce a kNN
approach that re-ranks documents based on their similarity with the query and
the documents the user considers relevant. Further, we explore Cross-Encoder
models that we pre-train using meta-learning and subsequently fine-tune for
each query, training only on the feedback documents. To evaluate our different
integration strategies, we transform four existing information retrieval
datasets into the relevance feedback scenario. Extensive experiments
demonstrate that integrating relevance feedback directly in neural re-ranking
models improves their performance, and fusing lexical ranking with our best
performing neural re-ranker outperforms all other methods by 5.2 nDCG@20.

本研究探讨如何将相关性反馈直接集成到神经重排模型中，使用 kNN 方法和交叉编码器模型，通过在现有信息检索数据集上的实验，证明了这种方法能够大幅提高神经重排模型的性能。

利用有限样本文档重新排序，加入相关反馈来改进信息检索

Incorporating Relevance Feedback for Information-Seeking Retrieval using Few-Shot Document Re-Ranking

With worldwide concerns surrounding the Severe Acute Respiratory Syndrome
Coronavirus 2 (SARS-CoV-2), there is a rapidly growing body of scientific
literature on the virus. Clinicians, researchers, and policy-makers need to be
able to search these articles effectively. In this work, we present a zero-shot
ranking algorithm that adapts to COVID-related scientific literature. Our
approach filters training data from another collection down to medical-related
queries, uses a neural re-ranking model pre-trained on scientific text
(SciBERT), and filters the target document collection. This approach ranks top
among zero-shot methods on the TREC COVID Round 1 leaderboard, and exhibits a
P@5 of 0.80 and an nDCG@10 of 0.68 when evaluated on both Round 1 and 2
judgments. Despite not relying on TREC-COVID data, our method outperforms
models that do. As one of the first search methods to thoroughly evaluate
COVID-19 search, we hope that this serves as a strong baseline and helps in the
global crisis.

提出了一种零样本排名算法，它适应于 COVID-19 相关的科学文献，它主要使用了一种预先训练在科学文本上的神经再排序模型 (SciBERT)，该方法在 TREC COVID Round 1 排行榜中排名最高且表现优异，在不依赖 TREC-COVID 数据的情况下，该方法的表现优于依赖这些数据的模型，是一种强大的全球危机搜索基线。

SLEDGE-Z: COVID-19 文献检索的零样本基准线

SLEDGE-Z: A Zero-Shot Baseline for COVID-19 Literature Search

Two step document ranking, where the initial retrieval is done by a classical
information retrieval method, followed by neural re-ranking model, is the new
standard. The best performance is achieved by using transformer-based models as
re-rankers, e.g., BERT. We employ Longformer, a BERT-like model for long
documents, on the MS MARCO document re-ranking task. The complete code used for
training the model can be found on:
this https URL

本文研究了使用传统信息检索方法进行初检索，然后采用神经网络再排序模型的两步文档排序，使用了适合长文档的 BERT 模型变种 Longformer 在 MS MARCO 文档再排序任务上获得了最佳性能。